Understanding Urban Cooling of Blue-Green Infrastructure: A Review of Geospatial Data and Planning Optimization Methods for Mitigating Urban Heat Islands

1. Introduction and Background

Climate change is causing increasingly frequent, intense, and prolonged heat waves (HWs) [1,2,3], which severely impact human society, quality of life, mortality [4,5,6,7], the economy [8], and ecosystems [9,10]. In cities, which feature unique land-use morphology and specific relationships between incoming and outgoing energy flows in the surface system, this trend is intensified by the formation of urban heat islands (UHI) [11,12,13,14]. Urban conditions, along with population aging, high population density, and concentrated infrastructure, make cities especially vulnerable to HWs' negative effects.

Spatial planning can regulate urbanization processes to mitigate HWs-related risks [15,16,17,18,19,20,21]. Within this sector, and in conjunction with landscape architecture, economics, and ecological engineering, the concept of ecosystem services has emerged. This concept represents the multidimensional benefits people gain from urban natural capital, based on the blue-green infrastructure (BGI) [22,23,24,25,26,27,28,29,30]. In this context, BGI was developed as a tool to counteract the negative effects of climate change, including UHI. The BGI consists of: (1) natural and artificial hydrographic elements, such as rivers, reservoirs, wetlands, and marshes; (2) all vegetated areas, including public parks and gardens, forests, squares, etc.; (3) vegetation-based hydrological-engineering solutions, including rain gardens, green roofs, and retention-infiltration basins [31,32].

The literature includes numerous studies evaluating the effectiveness of BGI in mitigating UHI, using related terms such as Park Cool Island, Water Cool Island, and Urban Cool Island (UCI), that refer to phenomena where specific BGI components have lower temperatures than impervious surfaces [33,34,35,36,37,38]. These studies have assessed BGI characteristics for their cooling potential, supporting sustainable spatial policies [39,40,41]. Unfortunately, the variety of research methods may limit optimal implementation, creating uncertainty and confining conclusions to local findings only [42]. The main categories of methods for assessing BGI effectiveness include: (1) on-site air temperature (T-air) measurements to evaluate “air” UHI mitigation; (2) remote sensing of land surface temperature (LST) related to surface UHI (SUHI), often using satellite data; and (3) numerical modeling of T-air at micro-, local, and mesoscale levels [13,42,43,44]. Moreover, it is notable that primary thermal data sources on urban areas, like LST and T-air, are not correlated [45,46,47,48,49,50,51]. Additionally, numerous indicators have emerged over time, defining BGI’s cooling potential in various ways.

This methodological diversity increases uncertainty in forming conclusions about effective BGI design, as it can become a key factor—alongside geographical, climatic, and weather conditions—in creating variation in results. Consequently, for example, research based on T-air generally indicates that vegetation provides a better cooling effect than water [52,53,54,55,56], while LST-based studies show the opposite [55,57,58]. Moreover, some studies indicate that the complexity of BGI shape may positively influence cooling potential [59,60,61,62,63], while others suggest a negative impact [39,41,58,64,65]. Furthermore, notable variation exists within each method type, where: (1) satellite remote sensing results show BGI cooling ranges between 18 and 4,000 m, depending on data sources and LST downscaling level [59,66,67,68]; (2) numerical simulations report cooling effectiveness from <1 to 18.4 °C, even after standardization based on vegetation fraction [42]; and (3) T-air field measurements indicate maximum cooling ranges of 180–860 m, with intensity levels between 0.14–8.8 °C [34,69,70,71,72,73,74].

For optimal planning and management of BGI, it is essential to organize and systematize existing research findings, including the potential impact of different research methods and data types. The wide variety of research techniques also points to the need for unified methodological frameworks to optimize BGI planning and management. Given advances in computational capabilities, these frameworks should leverage promising technologies such as Artificial Intelligence (AI), Big Data (BD), and the Internet of Things (IoT), which have the potential to enhance decision-support tools for BGI towards a more holistic approach. This framing can aid decision-makers and practitioners in interpreting results, and help researchers identify existing gaps and the most promising future research directions, ultimately leading to more effective BGI development strategies and stronger urban resilience against HWs.

In light of the aforementioned issues and uncertainties, the primary aim of this article is to systematize knowledge on the use of geoinformatics tools and spatial data in studying the cooling potential of BGI for spatial planning, through:

• Presenting the mechanisms behind the formation of BGI's cooling abilities;
• Discussing the most effective BGI features that positively impact cooling potential regardless of method;
• Examining the characteristics of spatial data and geoinformatics methods used in analyzing BGI's cooling effects, with attention to potential impacts on result variability;
• Proposing promising future research directions for optimizing BGI planning and management processes.

2. Materials and Methods

The methodology is based on a literature review, focusing on four areas: remote sensing, numerical simulations, field measurements, and emerging technologies. Literature searches were conducted on Google Scholar, Web of Science, and Scopus, up to November 2024, using keywords related to the quantitative assessment of urban BGI cooling by common methods. Various combinations of the following phrases were used for searching titles, keywords, and abstracts: “green”, “blue”, “infrastructure”, “water”, “vegetation”, “city”, “urban”, “heat island”, “cooling”, “cooling island”, “cooling potential”, “cooling effect”, “air temperature”, “land surface temperature”, “factors”, “method”, “technology”, “modeling”, “smart cities”, “remote sensing”, “simulation”, “field measurements”, “transect”, “traverse”, “morphology”, “local climate zones”, and “digital twin”.

The review included 287 references, selected for their relevance to: mechanisms and key features affecting BGI cooling effectiveness, characteristics of methods for quantifying BGI effectiveness that may influence the magnitude of cooling, and future research directions related to BGI management. Once the literature was selected, information was gathered at a progressive level (titles, then abstracts, keywords, and finally full texts), following criteria that: (1) the study is urban-focused, and (2) for objectives 2 and 3, it quantitatively assesses BGI features.

This review addresses BGI planning and management comprehensively—from current urban thermal assessment methods and mechanisms shaping BGI cooling to BGI characteristics that enhance cooling potential, considering both BGI-specific features and urban morphology. It also details geoinformatics methods for evaluating BGI’s cooling, covering BGI representation models, cooling potential metrics, spatial data sources, and potential tool development directions with a focus on promising technologies.

3. Existing Methods for Assessing the Thermal Characteristics of Urban Areas

To better understand how BGI cools urban spaces, it is essential to outline basic concepts describing urban thermal characteristics, particularly the UHI phenomenon. The UHI effect can arise from climatic processes within both the Urban Boundary Layer (UBL) and the Urban Canopy Layer (UCL) [75,76]. This effect is most intense under anticyclonic conditions, especially during heatwaves, when increased solar radiation raises the energy stored in the urban environment, and at night, when heat release from urban surfaces becomes the primary contributor [75,77]. UHI studies focus on two main areas: (1) "air" UHI, describing thermal differences within the atmospheric boundary and canopy layers, and (2) "surface" UHI (SUHI), analyzing temperature contrasts between urbanized and non-urbanized surfaces [13,43,44].

Measurements of the atmospheric UHI involve the use of sensors on fixed meteorological stations or mobile traverses to assess T-air differences within the UCL, or more advanced platforms such as tall towers, radiosondes, or aircraft-mounted temperature sensors to study the UBL heat island [44,78,79,80]. Relying solely on ground station T-air measurements can be inadequate for capturing fine-scale temperature variations within cities due to their sparse spatial distribution, often leading to an underestimation of temperature effects [81] However, with self-programmed measurements, it is possible to track the actual intensity of the UHI in dense time series and vertical profiles.

Conversely, SUHI research heavily relies on satellite-based thermal remote sensing data, affording consistent and replicable evaluations of LST dynamics [45,82,83]. LST reflects the energy exchange between the land surface, atmospheric insulation, and solar radiation [84,85,86]. This process influences near-surface T-air, evapotranspiration, and atmospheric humidity [87] affecting perceived temperature [88] However, relying solely on LST may be not sufficient for capturing the complex thermal dynamics of urban environments. Thermal sensors indirectly gauge temperature of the outermost layer of urban surfaces (tree canopies, rooftops etc.) by detecting upward long-wave radiation, which shaded areas under e.g., buildings and trees being undetectable [89,90], and various thermodynamic and radiative properties significantly impacting the detection [43,45,49]. Additionally, atmospheric absorption can consume radiation [48] and some atmospheric radiation may reach sensors without directly interacting with the surface [43]

In the cities, LST can be completely different from T-air which plays an important role in energy balance [45,91]. While some studies have confirmed a general relationship between LST and T-air in coarse scales [46,92,93,94,95,96,97,98,99,100], this cannot be applied to the morphologically complex urban spaces, due to solar intensity, surface moisture, near-surface atmospheric conditions, wind, clouds, shading, sky view factor, and sensor view angle [45,46,47,48,49,50,51]. These factors, along with sparse station T-air measurements, lack of sensor homogeneity, and coarse spatial resolution of model-derived urban T-air data, prevent the estimation of T-air from LST in urban environments [101,102,103,104].

Alongside real-world measurements, the literature also identifies an additional UHI research approach—numerical simulations of T-air and other urban atmospheric characteristics [42] Commonly used approaches within this methodology include local Computational Fluid Dynamics (CFD) [105,106] models and Energy Balance Models (EBM) [42,107,108], as well as mesoscale atmospheric models, such as WRF [42,109,110,111]. The most well-known CFD models include: OpenFOAM, FLUENT, STAR-CCM+, PHOENICS, and ENVI-met. In the case of EBMs, notable models include RayMan, SOLWEIG, green-CTTC, and TEB-Veg [108]

Despite the variety of methods available for assessing UHI and UCI, the most effective and, therefore, the most commonly used are: field measurements, satellite remote sensing, and CFD numerical simulations (for UCI primarily using ENVI-met).

4. Factors Determining the Cooling Potential of BGI

4.1. Micro-Scale Properties of BGI Affecting Cooling Potential

Through mechanisms like transpiration, shading, absorbing solar radiation for photosynthesis and modification of airflow, vegetation plays a pivotal role in urban energy balance, alleviating the UHI effect [77,112,113,114,115]. Evapotranspiration through BGI development can replace sensible heating with latent heating, leading to evaporative cooling, effectively lowering both surface and ambient temperatures [112] The cooling process of urban space by vegetation is shown in Figure 1.

The cooling potential of transpiration can vary based on factors such as plant species, leaf characteristics, stomatal resistance, and soil conditions [70,117,118]. Additionally, transpiration rates are influenced by weather and climate conditions, with plants actively regulating stomatal openings to manage heat stress and water loss [77,119]. Prolonged heat waves and drought conditions can reduce the effectiveness of evaporative cooling [77,120] and may cause vegetation to release carbon dioxide to the atmosphere [121] Urban vegetation also provides shading, that intercept solar radiation, limiting heat absorption and minimizing heat re-radiation by urban surfaces [115,116]. Shading effectiveness depends on factors like leaf size and density, and canopy density [122,123]. Additionally, greenspaces modify local wind patterns, facilitating convective heat exchange and dissipating heat flux, further enhancing cooling effects [75] In this context, studies suggest that the dispersed arrangement of tree canopies may be more effective [124,125], additionally preventing heat retention, and maintaining optimal relative humidity levels, especially at night [77] Moreover, vegetation indirectly assists climate cooling by filtering pollutants through dry deposition and pollutant absorption [77] These processes reduce atmospheric scattering and absorption of radiation, impacting the radiation balance and T-air [126]

Studies on the cooling benefits of BI are fewer compared to greenspace, focusing primarily on daytime temperature effects and LST differences, which may not fully capture the conversion of sensible heating to latent heating [77,127]. BI can reduce the daily UHI effect due to water’s high heat capacity and low thermal conductivity, as well as evaporation properties, leading to the so-called “thermostatic effect” [75,128]. Shading, reflectance, and thermal inertia play significant roles in BI cooling [75,113], with wind velocity enhancing convective heat transfer and evaporation rates [129] Dynamic water bodies like rivers carry heat downstream, affecting local temperatures [130] while static water bodies, like lakes, exhibit thermal stratification, influencing their cooling efficiency based on their size and depth [75,131].

Comparative assessments of greenspace and bluespace integrated cooling abilities are rare in climate studies. Some research indicates significant cooling effects within street canyons with access to riparian greenery, while also showing variability in cooling potential based on factors such as river water temperature, incident solar radiation, wind speed, and relative humidity [132] Moreover, riparian vegetation has been shown to reduce the net radiation balance and T-air over streams [133] However, vegetation can create windbreaks, promoting moisture accumulation above water surfaces, which may lead to the formation of warm, humid pockets in the 'quiet zone' downwind, negatively impacting cooling efficiency [134]

4.2. Local-Scale Properties of Bgi Affecting Cooling Potential

Studies on UCI extent most frequently identify BGI size [34,135,136,137,138], vegetation intensity and quality [139,140,141,142] (with grasslands showing the weakest efficiency [41], adequate irrigation [33,41,143], proportion of forest vegetation [40] and the presence of surface water within BGI boundaries [41,144,145,146] as the features most strongly influencing the UCI extent provided by BGI in local scale. A key factor influencing the overall cooling effect may also be surface roughness, which depends on vegetation arrangements and affects ventilation characteristics [77] Larger BGIs can create greater temperature and humidity contrasts with built-up areas, enhancing the park-breeze effect [72,77,116,147]. Foliage and vegetation condition may enhance cooling effects by: (1) enhancing evaporative cooling [70,112,118] due to intensified photosynthesis [77,120], (2) reducing solar radiation absorption due to shading [116,122,148], and (3) boosting convective cooling, by raising canopy surface roughness [75,77]. Better efficiency in areas integrating greenspace with water may result from the synergistic capabilities of evaporative cooling and reduced solar radiation absorption by BI through shading by vegetation, which, along with internal convection processes and water mixing in reservoirs, further enhances BI's ability to absorb surrounding heat [77] Additionally, the proximity of BI ensures better access to groundwater, strengthening the resilience of GI to drought and increasing the cooling capacity of vegetation [117,119,120].

The complexity of BGI shapes can either positively [59,60,61,62,63] or negatively [39,41,58,64,65] impact the cooling range, depending on the adopted scale [58] climatic zone, or type of development. This complexity can weaken the cooling effects of vegetation located at the boundary of complexly shaped BGI areas by amplifying thermal stress, leading to stomatal closure [77,148], which can be dependent on surrounding development settings. Additionally, the efficiency of surface water compared to vegetation remains a topic of debate [149,150]—it may be greater in terms of LST [55,57,58,63,151] and lower in terms of T-air [52,53,54,55,56]. Regarding BI, the size and shape also influence its cooling efficiency, with larger bodies with complex shapes generally providing more significant cooling [57,58,152]. Latitude, seasonal, and diurnal variations also affect cooling intensity, which is stronger in lower latitudes and varies throughout the day and year [153,154,155].

The spatial configuration and composition may also influence the UCI intensity [156,157]. BGI objects situated in the highest spatial density may interact with their neighboring facilities, limiting each other's cooling zones [138,158,159], which demonstrates the collective cooling capabilities of small spatially concentrated BGIs. In this context, the characteristics of BGI objects can be examined in terms of landscape metrics. The most significant feature may be the level of aggregation and connectivity of BGI objects [68,160]. Additionally, the combination of edge density and patch density may exert a high influence on UCI [157] Spatial arrangement has also proven to be important in case of BI, with large water bodies presenting a higher cooling effect compared to equally distributed small water bodies [161]

The examples of the discussed features are presented in Tables 1, 3, and 4.

4.3. Urban Morphology Impact

The cooling potential of BGI is influenced not only by its internal characteristics but also by the surrounding urban areas morphology [62,129,138,162]. Through the modification of airflow, atmospheric heat transport, radiation balances, albedo, moisture availability, and surface heating potential [163] the built environment affects evaporative cooling [70,112,118] and convective cooling [75,77], regulating the intensity of the so-called park-breeze effect [72,77,116], which allows the transfer of cooler air to built-up areas due to the temperature difference between the BGI and the urban surfaces (Figure 2).

The primary factors by which building types influence BGI cooling potential are air humidity and temperature contrasts, which regulate the intensity of the park-breeze effect [72,77,116]. Urban structures also modify wind conditions, affecting the strength of convective cooling [164,165]. Additionally, urban development can induce thermal stress on plants, potentially leading to stomatal closure and limiting evaporative cooling during extreme temperatures [70,114,165], and can also impact drought risk, which reduces BGI cooling efficiency [70,166,167]. Thus, denser building patterns may both enhance the park-breeze effect [117,165,168,169], as well as increase plant thermal stress and limit water access, ultimately diminishing cooling potential.

The most comprehensive model for classifying urban morphology in terms of characteristics relevant to BGI cooling capabilities (e.g., emissivity, reflectivity, conductivity, absorptivity, and sensible heat storage) is the Local Climate Zones (LCZ) classification system developed by Stewart and Oke [163] This model enables the delineation of homogeneous urban areas based on surface structure and cover, building types, materials, and human activity. LCZs have a distinct screen-height temperature regime, particularly noticeable over dry surfaces during calm nights in areas with uniform landform [163] In addition to screen-height temperature, LCZs also define homogeneous zones by air humidity regime [170]and local-scale urban ventilation performance [171] thus distinguishing urban spaces by their actual potential for BGI evaporative and convective cooling. Notably, initially designed to define UBL UHI more accurately [163,172,173], due to the consistent delimitation of homogeneous areas in terms of natural and artificial materials (like emissivity, reflectivity, conductivity, absorptivity and sensible heat storage capacity), LCZ can also be successfully applied in LST-based studies [43,174,175,176,177,178,179].

5. Characteristics of Selected Geoinformatics Methods and Spatial Data Used to Assess the Cooling Potential of BGI

5.1. UCI Studies Based on Satellite Remote Sensing

The remote sensing approach is based on LST. These data are typically obtained through satellite remote sensing using passive sensors that detect both the reflected shortwave radiation reflected from the land surface and the emitted longwave (thermal infrared) radiation [180]

5.1.1. Methods for Creating BGI Representation

In remote sensing studies, two primary types of BGI representation are distinguished: continuous and discrete. Continuous BGI representation is achieved through vegetation indices such as the normalized difference vegetation index (NDVI), which illustrate the spatial distribution and intensity of BGI presence. Here, each raster pixel is treated as an individual sample, making the analysis of BGI efficiency largely based on correlating the BGI raster with the LST raster [181] Discrete representation, on the other hand, involves clearly defined boundaries for BGI areas, allowing differentiation between vegetation and surface water [59] However, accurately delineating BGI boundaries can be challenging. Common methods include supervised classification and reclassification of vegetation indices (e.g., NDVI). In this model, each BGI feature is treated as a single sample. The method allows the use of predefined urban data (e.g., only publicly accessible green spaces). In SUHI analyses, less common representation methods have also emerged, offering unique advantages, such as the "cooling network and cooling source areas" model, which organizes information on BGI features through morphological spatial pattern analysis (MSPA) [156]

5.1.2. Methods for Assessing the Cooling Potential of BGI

The cooling extent directly influenced by BGI is challenging to evaluate, as it depends on both BGI characteristics and the thermal conditions of adjacent built-up areas [158,182]. Common approaches focus on identifying the first turning point in the LST-distance function from the edge of a BGI feature [59,64,151,166,183,184]. This approach mainly aims to define the cooling range of BGI (HCD), which ends at the first inflection point on the function, and cooling intensity (HCI), measured as the LST difference between the first turning point and the BGI boundary. Calculating these indicators typically involves various spatial analyses, with popular methods including the assessment of LST increase based on zonal means in buffer zones around BGI features [66,166]. LST values can also be collected along straight lines extending outward from BGI boundaries, in cross-sections [59] to evaluate potential variations in UCI extent direction. More advanced techniques like the semivariance function [158,185] or the use of watershed algorithms that treat LST as terrain elevation, where watersheds represent cooling zones, are also applied [138] Surface-based methods are most effective, as they capture directional cooling influences, though validation against control measurements is essential for accuracy.

5.1.3. Remote Sensing Data Used in UCI Studies

The most commonly used data sources for LST calculations include the Landsat satellite series, the MODIS instrument, and ASTER [43] The capability for thermal radiation detection began in 1982 with the launch of the Thematic Mapper (TM) sensor on the Landsat 4 satellite, which imaged the Earth's surface in thermal infrared at a spatial resolution of 120 m [186]. The latest generations of Landsat (8 and 9) can generate LST data using the Thermal Infra-Red Sensor (TIRS), which a resolution of 100 m, with the possibility of resampling to 30 m to match the bands of the Operational Land Imager (OLI) [187] Since 2008, Landsat data has been freely available. A single image covers an area of 185 x 185 km, allowing for the analysis of entire urban areas based on data from a single time point. For Landsat 5, 7, 8, and 9, the revisit cycle for the same area is 16 days, with Landsat 8 and 9 having revisit schedules that enable increasing the temporal resolution to 8 days. This allows for the analysis of UCI changes over time with high frequency, enabling the recording of the impact of land use changes on urban thermal conditions. Landsat is one of the most effective sources of information on the thermal characteristics of urban areas.

The second most popular source of LST data is MODIS, an instrument mounted on the Terra and Aqua satellites [43,188,189,190], which were launched in 1999 and 2002. The temporal resolution of MODIS data is 1-2 days, which allows for the creation of high-frequency time series, but the spatial resolution is only 1 km, limiting the ability to conduct detailed analyses of the cooling potential of individual BGI objects. However, due to the large size of each image (a 2330 km wide strip), MODIS is widely used in studies of large areas [43,191,192]. Additionally, MODIS provides many ready-to-use products, including daytime and nighttime LST and emissivity data [193,194]. MODIS data is freely available.

Another source of LST data is ASTER, located on the Terra satellite [43,195]. These data were collected during the radiometer's operational period from 1999 to 2016. ASTER imaged the Earth's surface in 14 bands, with 4 of them capturing the thermal infrared spectrum at a spatial resolution of 90 m [196] This instrument provides access to the Surface Kinetic Temperature product, available for both daytime and nighttime [43,197]. Like Landsat and MODIS, ASTER data are also freely available. However, these data are rarely used in studies of UCI extent.

In recent years, LST data from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) sensor have gained increasing popularity [51,198,199]. This sensor was launched to the International Space Station (ISS) on June 29, 2018, with a temporal repeat cycle of 3-5 days, depending on latitude [200] ECOSTRESS images the Earth's surface in five bands, providing LST with a spatial resolution of 38 m x 68 m, with a single image covering an area of 402 km (53°) [51] The main advantage of ECOSTRESS data is the inclined, precessing ISS orbit, which allows for imaging the same area at different times of the day and night, enabling the analysis of diurnal variations [200] Additionally, the five channels enable more accurate multispectral Temperature Emissivity Separation (TES) approaches for LST calculations [51,201]. ECOSTRESS data are freely available. Due to its relatively recent launch, these data have not yet been used to study the thermal effectiveness of BGI characteristics.

Additional useful data for BGI research include: PlanetScope SuperDove (free for research purposes, 3 m resolution, 8 bands, ideal for time-series BGI and vegetation health classification); WorldView (paid, up to 0.3 m resolution, 16 bands, excellent for BGI and artificial materials classification); IKONOS, QuickBird, GeoEye-1, Pleiades-1, SPOT-5/6/7, Gaofen, and SkySat (effective for BGI classification); Prisma (30 m hyperspectral data for vegetation species classification); EnMap (high-resolution hyperspectral data); Sentinel-5P (free, suitable for air pollution time-series mapping); Sentinel-2 (free, good for large-scale BGI classification and vegetation health assessments); and ERA5 (free, high temporal resolution weather model).

Examples of research results on BGI features affecting its cooling potential using remote sensing techniques are presented in Table 1.

5.2. UCI Studies Based on Numerical Simulations

The most effective approach for modeling the cooling potential of BGI is CFD models due to their ability to simulate dynamic changes in system elements [106] CFD produces quantitative predictions of fluid-flow phenomena based on the conservation laws (conservation of mass, momentum, and energy) governing fluid motion [105] CFD models are based on interactions between urban fabric elements and local atmospheric parameters. These simulations account for shortwave and longwave radiation influx and outflux, anthropogenic heat, energy storage in artificial materials, evaporation, and heat exchange and transport by wind [207] CFD results differ from remote sensing studies mainly due to limited computational capacity, which hinders comprehensive study of large-scale characteristics like surface area, shape complexity, and the spatial configuration of larger BGI areas. Numerical simulations usually focus on smaller-scale BGI elements like tree groves, lawns, or green roofs and walls.

One of the most effective CFD models for BGI analysis is ENVI-met, designed to simulate surface-plant-air interactions in urban environments [106,110,208,209] and daily climate cycles [209] It is a three-dimensional microclimate model based on a voxel-grid, using Reynolds Averaged Navier-Stokes equations for wind flow and an indexed view sphere scheme for radiative fluxes [106] The simulation requires two input files: one defining area layout (buildings, vegetation, soil) and another with meteorological settings and output parameters. Newer ENVI-met generations allow defining thermal mass and heat inertia on buildings, as well as temporal variations in T-air and relative humidity [209,210].

Despite its advancements, ENVI-met still has some limitations. These include issues with radiation flux calculations, turbulence modeling which tends to overestimate the turbulent production in high acceleration areas, the grid generation, lack of information about near-wall phenomena, the inability to fully account for atmospheric flow characteristics, and the assumption of constant cloudiness and wind conditions [106,211,212,213,214].

An alternative to the commercial ENVI-met software is Ladybug Tools, which integrates its modules Butterfly and Honeybee. Butterfly is a plugin for Grasshopper/Dynamo and a Python library based on an object-oriented paradigm. It is built on OpenFOAM, an open-source CFD engine capable of running simulations and turbulence models, from simple RAS (Reynolds-Averaged Simulation) models to more complex LES (Large Eddy Simulation) models [215,216]. Honeybee, on the other hand, supports detailed daylighting and thermodynamics modeling, also being an open Python library. It allows the creation, execution, and visualization of daylighting and radiation simulations using the Radiance engine, as well as energy simulations with EnergyPlus/OpenStudio [215,216].

5.2.1. Methods for Creating BGI Representation

Both OpenFOAM and the Simple Plant Module of ENVI-met use statistical methods to describe vegetation morphology through LAI [107] ENVI-met also includes a 3D-Plant module, which creates representations of canopy geometry as a mesh that approximates the shape and location of objects, incorporating Leaf Area Density (LAD) and Root Area Density [217] In addition to aerodynamic characteristics, vegetation has radiation effects, described by transparency that influences the transmission of solar radiation [106,107,108]. Water bodies are often represented by a specific soil type, partially transparent to shortwave radiation [218] with defined heat conductivity, heat capacity, and depth. Computational processes within the water body model consider the transmission and absorption of shortwave radiation [218] However, no secondary energy balance or supplementary boundary conditions are applied to the water or ground surfaces. As such, water grids are assumed to be deep enough to absorb most of the shortwave radiation. Some models, like ENVI-met 4.0, also simulate water spray effects, enabling the modeling of fountains and cooling through air-water aerosols [219]

5.2.2. Methods for Assessing the Cooling Potential of BGI

Analyses based on Computational Fluid Dynamics often involve comparing spatial development scenarios in terms of mean T-air or thermal comfort indices for the study area in both baseline and post-BGI implementation conditions, focusing on features such as spatial layout, coverage configuration, and tree density [220,221,222,223,224]. Studies assessing the maximum cooling range are rare [223] and evaluations of the area directly affected by BGI's cooling capacity are not typically performed.

In addition to T-air, CFD-based studies can also assess variations in mean radiant temperature (T-mrt), LST [182,210,225,226,227,228], and Human Thermal Comfort Indices, such as the physiological equivalent temperature (PET) index [210] which depends on T-air, humidity, wind speed, radiation fluxes, and individual factors like body energy balance, gender, age, weight, and clothing [222] Another example is the Universal Thermal Climate Index (UTCI), which provides an equivalent temperature incorporating T-air, wind speed, humidity, T-mrt, and typical clothing based on weather conditions [229]

5.2.3. Data Used for ENVI-Met Simulation

In ENVI-met, the spatial configuration file must include information on the location, dimensions, and materials of building walls and roofs. Additionally, details about vegetation, surface characteristics, and soil properties in undeveloped areas are required. Spatial parameters can be obtained through remote sensing, significantly reducing field data collection time. High spatial and spectral resolution remote sensing data are needed to ensure model accuracy. The model also requires meteorological parameters such as wind speed and direction, relative humidity, and surface roughness to initialize the simulation [230] Table 2 presents the main input parameters required by ENVI-met along with potential data sources.

Examples of research results on BGI characteristics influencing its cooling potential using numerical simulations are presented in Table 3.

5.3. UCI Studies Based on Field Measurements

In field measurements of UCI, the evaluation of cooling effects can be performed through T-air and other atmospheric parameters measurements, either stationary or using mobile sensors. Typically, characteristics like LAI, vegetation height, and crown density are examined in relation to meteorological conditions and their bioclimatic impact [242] Less frequently, the size of the BGI area or the proportion of tall greenery is examined [243] However, due to the need to make measurements at individual points, these methods usually focus on a micro or local scale, typically involving only a few BGI objects [244] Nevertheless, studies based on manual measurements allow for vertical T-air profile analysis, which improves the method's value over remote sensing, allowing for inferences about the causes of advection heat exchange between BGI and urban areas.

In field studies, the most commonly measured parameter is T-air, with PET being less frequent. Common UCI measurement methods include: (1) comparing the average temperature inside a BGI element with the surrounding temperature [34]and (2) creating "cross-sections" through the BGI object and its surroundings from measurement points (mobile traverse/transect method) and evaluating the resulting function [33,244,245]. The second method uses mobile temperature loggers with GPS mounted on various vehicles, including bicycles [33] This method allows for multiple measurements with a single device in a short period, reducing sensor installation costs while ensuring measurement homogeneity. Calibration of results based on temporal changes of the measured parameters is necessary using the optimized reentry-transect method [244] To maintain measurement point simultaneity, transects should be created with no more than a 2-hour time gap. Often, multiple sensors are mounted on one vehicle to increase measurement frequency and accuracy. In addition to T-air, other parameters such as black globe temperature, relative humidity, wind speed, wind direction, and solar radiation are also measured [244]

Examples of research results on the features of BGI affecting its cooling potential using field measurements are presented in Table 4.

6. Technologies with the Potential to Develop Tools to Optimize BGI Planning and Management

In spatial planning, BGI is one of the most effective tools for mitigating and adapting to climate change. Adaptation, in addition to reducing thermal stress, includes alleviating the intensity of flash floods [248] enhancing resilience to urban droughts [249] air purification [250] and increasing biodiversity [251] Mitigation using BGI mainly involves carbon dioxide sequestration [19]and energy consumption reduction [252] Optimal BGI implementation requires considering the relationships between all potential outcomes, not just urban thermal issues, ensuring that the solutions are as effective as possible overall. Given the complexity of the problem, tools based solely on numerical simulations, remote sensing, and other measurements become insufficient. In this context, more complex systems and technologies are necessary, such as Digital Twin (DT). DT is defined as “a virtual representation of a physical system (and its associated environment and processes) that is updated through the exchange of information between the physical and virtual systems” [253] A key element of DT is ensuring connectivity between the model and the real world. DT is closely linked with Artificial Intelligence (AI), Big Data (BD), Internet of Things (IoT), and edge processing for data collection and processing, enabling real-time system monitoring as well as simulating changes under specific parameter modification scenarios. Although the term DT was first mentioned in the early 21st century, it has only become widespread in cities in recent years, thanks to the development of digital infrastructure and data-based technologies [254] becoming an integral part of the smart city concept [254,255].

Urban DTs are used for power grids, energy demand, renewable energy, mobility, public transportation, road infrastructure, water supply, sewage, noise pollution, flooding, and climatology [254,255,256,257]. Most research focuses on their conceptualization or application within narrow industry frameworks [253,255,258,259,260]. However, there is limited knowledge on creation and implementation of comprehensive DTs. In the context of BGI, despite the enormous potential and developmental capabilities of DT technology, no comprehensive system has yet been implemented for its planning and management at the urban scale, considering all ecosystem services and the level of detail needed for local planning.

DT technology for BGI planning and management should encapsulate data on key processes in urban environments across various scales. Ensuring interoperability between systems is crucial for effective information exchange and better decision-making through more accurate forecasting. A promising approach is AI for managing IoT (AIoT) [261] Current interoperability challenges include data silos from infrastructure providers [262] isolation in public sectors [263] and tight coupling of data to specific applications [257,264], along with the complexity of multiple scales and domains [265] Additionally, data heterogeneity in scale, detail, accuracy, and spatial and temporal uniformity, combined with BGI's complex relationships with urban space, makes defining semantic rules for dynamic city representation difficult [266,267]. Therefore, creating new metadata standards and universal data exchange formats is necessary for effective data fusion [254,268,269].

Infrastructure is crucial, including computing, networks, and data storage [254] DT technologies, such as IoT [270]and increasingly detailed 3D models with Augmented Reality (AR) [271] require highly efficient real-time data processing [270] This necessitates the development of new data processing engines [272]or edge computing architectures [273] along with higher-capacity networks. Beyond processing, data storage and archiving vast amounts of data used by DT become critical [264] A promising direction is the development of efficient decentralized data storage architectures [273] A key issue is also data acquisition [254] Current IoT networks (e.g., Open Sense Map) may be too fragmented and heterogeneous to maintain proper temporal and spatial continuity [264] Further challenges include synchronizing real-time measurement frequencies [255] network speed, and connectivity continuity. Synchronization affects data generation, distribution, and analysis speed, which is crucial for real-time processing [254] Additionally, despite the existence of various IoT networks, some environmental, social, or economic data are still missing, and some lack sufficient quality and reliability to ensure the accuracy and realism of DT [264,274,275].

A key challenge is ensuring an optimal modeling and simulation system for evaluating urban development scenarios. This is crucial for improving decision-support systems [254] Promising advancements in machine learning and deep learning (ML/DL) are being explored for processing data in DT [275] However, challenges remain with computational limitations, integrating models at multiple scales, ensuring data quality [272,273], and managing the complexity of urban systems [276] along with the lack of sufficient validation [264] uncertainty quantification [277] and adaptation to extreme situations [266] Maintaining informative visualizations of results is also essential, with potential in technologies such as AR, Extended (XR), Virtual (VR), and Mixed Reality (MR) [254]

Beyond technological challenges, the development and implementation of DT for BGI may be hindered by the need for a skilled workforce and funding to develop and maintain the infrastructure [254] Additionally, the diverse and dispersed nature of BGI may require various sub-models to accurately simulate elements like water, vegetation, soil, heat exchange, or flood risk. Despite these challenges, implementing DT technology in cities can significantly improve the efficiency of decision-making processes related to BGI planning and management [278] Research focused on the development of DT for BGI should therefore be a priority in optimizing planning and management methods, both in conceptualization and in the construction and integration of specific geoinformatics products and spatial data to support the creation of more resilient cities.

7. Discussion

7.1. Interpretation and Findings

7.1.1. Differences Between Results Obtained Using Different Approaches

Each approach to assessing cooling potential—remote sensing, numerical simulations, and field measurements—offer unique capabilities for analyzing BGI [110] Numerical simulations require the most data and significant computational power, even for analyzing small areas [42] Remote sensing methods, on the other hand, can often rely on readily available products covering thousands of square kilometers [43] However, remote sensing is limited to measuring LST within the sensor’s specific angle, restricting information about urban thermal conditions, especially in areas with complex morphology [51] Field measurements require minimal data, have a high temporal resolution and are suitable for analyzing short-term trends, but are limited by the accessibility of certain urban locations, such as private properties [279]

The research objective and scale of analysis are key in selecting the appropriate method. At the local scale, if the goal is to assess BGI characteristics relevant to urban planning (e.g., BGI area geometry, vegetation density, and urban morphology impact), remote sensing is the best choice. It captures broad spatial context, allowing analysis of multiple large BGI areas simultaneously, though it is effective only for evaluating existing BGI features. At the neighborhood scale, when evaluating the effectiveness of different BGI development scenarios, numerical simulations are optimal. This method supports micro-scale analysis, such as assessing solutions like green roofs and walls. Field measurements, on the other hand, excel in capturing actual atmospheric parameters necessary for calculating thermal comfort indices, providing insight into BGI’s real impact on urban livability.

Aside from diverse urban morphology and meteorological and climatic conditions, variations in results may stem from differing definitions of BGI cooling effectiveness. For remote sensing studies, comparisons typically involve contrasting LST values within or at the edge of a BGI feature with those of the immediate surroundings, e.g., [66] Field measurements use the same method but focusing on T-air differences, e.g., [33] In numerical simulations, cooling effectiveness is often reflected by differences in average T-air across the study area before and after introducing specific BGI configurations, e.g., [221] The latter approach simulates BGI's potential impact on urban temperature reduction, offering insights into cooling effectiveness, though the results inherently carry some uncertainty.

LST resolution notably impacts results in estimations using remote sensing. Studies using lower-resolution data report broader cooling extents than those based on high-resolution LST [68] indicating that "mixed pixels" might artificially expand cooling zones. This effect is particularly evident with common Landsat 8/9 data, which use TIRS images with a native 100 m resolution, downscaled to 30 m. Each 100 m pixel averages diverse urban surfaces, thus blurring BGI boundaries and potentially intensifying the gradient effect of rising LST with distance from the BGI site. Additionally, the land use around the BGI can reinforce the LST gradient effect if smaller vegetation structures, like single trees or green roofs, reflect a gradient of decreasing vegetation density with distance. Building configurations that cast shadows also influence localized LST values, potentially lowering pixel readings. These challenges become less pronounced with higher-resolution imagery. Downscaled images, however, may introduce errors associated with the statistical resolution enhancement. Results may also vary due to cooling potential calculation methods, including buffer widths and other methods used.

Another important factor affecting results is the method of BGI delimitation. Using BGI boundaries from urban databases often overlooks the actual extent of vegetation in favor of property boundaries. Such approach is highly susceptible to errors caused by the surrounding morphology impacting cooling zone anomalies. More effective methods involve processing remote sensing imagery (e.g., image classification or vegetation index reclassification [66], allowing for objective, uniform delineation of BGI's physical boundaries. It is important to use the same imagery source for both LST calculations and BGI delimitation (e.g., Landsat 8/9—OLI bands for BGI delimitation and TIRS bands for LST calculations), minimizing errors due to georeferencing or radiometric corrections.

In numerical simulations, we confirmed that the largest discrepancies occur between micro- and mesoscale models [42] This is primarily because models at different scales apply distinct assumptions regarding physical heat exchange processes in urban spaces. Mesoscale models often simplify local phenomena, such as the cooling effect of individual trees or small green spaces, and cannot fully describe heat fluxes in urban canyons [110] Conversely, microscale models offer more detail and can better represent these localized processes, yet they are limited in generalizing results across larger areas.

Significant differences can also be observed among models at the same scale, despite simulating identical scenarios and conditions. This variability often results from differences in how models implement and parameterize physical processes related to BGI’s cooling mechanisms in urban spaces. Models may vary in their representation of vertical heat transport or surface-atmosphere energy exchanges, which impacts outcomes.

Furthermore, a model’s ability to accurately recreate the microclimatic conditions of a specific area does not ensure its suitability for simulating BGI cooling potential. Cooling effects from evapotranspiration and shading require specific sub-models, which are not typically assessed in standard numerical simulation tests. Thus, for UCI studies, model physics evaluation should be conducted with stricter criteria to enhance the reliability of simulation results [42]

In field-based studies, differences in results may arise from how microclimatic factors are recorded, such as how sensors are shielded from solar radiation. Variations can also stem from the method of data collection itself. Results can differ between studies based on data from fixed monitoring stations and those using mobile recorders, as the latter may be more precise [110] Additionally, differences may arise from time shifts in measurements when using mobile traverse methods. It is evident that without accounting for changes in T-air over the daily cycle, measurements taken along a transect from the studied BGI object outward may show an increasing trend, even if the object itself does not exhibit cooling properties. Therefore, the use of the optimized reentry-transect method is necessary [244]

7.1.2. BGI Characteristics Affecting Cooling Potential

BGI is an effective tool for mitigating both LST and T-air, improving thermal comfort and enhancing the quality of urban life and energy efficiency. In LST-based studies, the most commonly analyzed BGI characteristics impacting cooling efficiency include geometry (area, perimeter, LSI, and other perimeter/surface ratios), canopy cover, and surface water fraction [41,59,62,166,183]. Numerical simulation studies often focus on vegetation fraction and various BGI configurations (including trees and green roofs) [210,220,221,237,241]. Field measurement studies typically examine BGI surface area and the proportion of tall vegetation [33,34,243,247]. In all cases, the most important feature is the BGI area. Most studies have shown that larger BGI objects (defined as vegetation-covered areas) cool urban spaces more intensively and over a larger scale [138,203,204,205]. However, the cooling capacity increase is not proportional to the increase in BGI area. Studies suggest the need to identify the minimum area that ensures effective cooling [55] balancing economic viability and efficient space use. Besides the BGI scale, vegetation intensity and quality are essential. In remote sensing, NDVI [62,158] or LAI can define this characteristic; in numerical simulations, LAI, LAD, or vegetation type diversity level [223,224,232]; and in field measurements, the proportion of tall vegetation [33,34,243,247]. However, adequate ventilation is also crucial, so vegetation should not be overly dense [125]

Additionally, specific characteristics that can only be assessed using a single method type can be distinguished. For example, comparing the cooling efficiency of green walls and roofs to traditional BGI is feasible only through numerical simulations. Although green roofs help regulate local temperatures, they are less effective than conventional BGI [182,220,226], limiting their cost-effectiveness to dense urban settings where creating ground-level BGI is not feasible. Another group of BGI characteristics are landscape metrics, which can only be assessed through remote sensing. In this context, the most important factor is the spatial connection of BGI [68,160].

The complexity of BGI shapes is often studied, but the results are inconsistent. The differences may stem from varying locations and climatic characteristics of the studied areas [143,166,280], and different interpretations of statistical analysis results. Commonly studied indicators like LSI often correlate strongly with shape area, leading to potential misinterpretations that larger LSI implies greater cooling. An additional source of uncertainty may be the varied urban morphology, which can modify the cooling potential of specific BGI shapes [72,77,116,147,148]. Therefore, when evaluating the impact of shape complexity, it is important to first confirm the lack of correlation with shape area and assess the effect of surrounding building morphology.

7.2. Gaps and Future Research Directions

7.2.1. Incorporating Factors Related to Urban Morphology Around BGI

Studies on the cooling capacity of BGI features rarely address characteristics of surrounding urban morphology, despite its potential impact on BGI's cooling ability by increasing temperature and humidity contrasts [72,77,116,183,281] and thermal stress on vegetation [40,148]. Future research should place more emphasis on this aspect. The focus should be on utilizing the LCZ concept. A useful tool for remote sensing could be the World Urban Database and Access Portal Tools LCZ Generator [282] which enables semi-automatic classification of urban areas based on morphological diversity relevant to thermal dynamics.

7.2.2. Development of an Objective Method for Delimiting BGI Objects for Analysis

Regardless of the adopted definition of cooling, the delimitation of BGI shapes has the greatest impact on the obtained results. Therefore, it is essential to use objective methods for delimiting BGI objects. One such method can be Geographic Object-based Image Analysis (GEOBIA) [283,284], preferably performed using the same image employed to calculate the LST data.

7.2.3. Integration of New Spatial Data

An important direction should be the integration of new, less-explored spatial data, such as ECOSTRESS LST. Such analyses could shed new light on the changes in the cooling potential of BGI over a 24-hour cycle, including at night, which would be important, for example, in assessing the impact of BI on nocturnal SUHI. It is also necessary to aim for higher-resolution LST data [166]and greater engagement of IoT, which could assist in both better validation of CFD studies and the accuracy of T-air estimations from LST using geostatistical techniques. Additionally, UAV measurements [285]offer promising potential for validating simulations and providing precise thermal data, especially for surfaces inaccessible by standard remote sensing, enabling more accurate assessments of BGI characteristics.

7.2.4. Research on a Large Scale or the Integration of Results into a Universal Indicator

Different climates and geographic locations can significantly impact the results of BGI's cooling potential. An objective assessment of BGI characteristics requires analyzing cases from various contexts. To achieve this, studies should be expanded based on automated analytical procedures that allow for the simultaneous analysis of multiple cities using a unified method (e.g., [286], or through literature reviews that incorporate the standardization of results, e.g., [42]

7.2.5. Correct Interpretation of Results Obtained Using LST

The results of remote sensing analyses do not represent perceived temperature but rather LST, influenced by emissivity and other physical parameters of different surface configurations. BGI affects LST primarily through shading and evapotranspiration, which is well documented. However, the cooling mechanisms of LST in areas distant from BGI require further research and validation to address resolution and urban morphology issues.

7.2.6. Development of Comprehensive Urban Ventilation Models

Wind conditions modify BGI cooling capacity and thermal comfort. Effective BGI management requires detailed city-wide ventilation simulations that consider urban morphology and BGI features. However, current CFD models are scale-limited, partly due to computational constraints. Therefore, advancing CFD and related technologies with AI support (e.g., graph neural networks [287] is essential.

7.2.7. Use of the Digital Twin Technology

There is a need to unify approaches for assessing the cooling potential of BGIs and standardize data for interoperable solutions. Future BGI research should move away from traditional methodologies towards revolutionary concepts like DT. It is essential to combine AI, IoT, and BD technologies with the practical needs of implementing DT in cities for optimizing BGI planning and management. The main challenges include: (1) interoperability and semantics; (2) computational infrastructure, networks, and data storage; (3) data acquisition, synchronization, and updating; (4) data quality and harmonization; (5) modeling and simulation of real phenomena, and integration with decision-making systems; (6) visualization informativeness; (7) human and financial resources; (8) legislative, organizational, and social issues [254] These topics require the most attention in future research to enable the creation and implementation of holistic tools for optimizing BGI planning and management to enhance cities' resilience to climate change.

8. Conclusions

This study aimed to systematize knowledge through a literature review on using geoinformatics tools and spatial data to assess BGI cooling potential for spatial planning optimization. The objectives were to: (1) explain the mechanisms behind BGI cooling, (2) discuss effective BGI features, (3) analyze the impact of spatial data and methods on results, and (4) suggest future research directions.

BGI cooling mainly comes from vegetation's evapotranspiration, shading, and wind flow modification, which enable evapotranspiration and convective cooling. The surrounding urban morphology also affects cooling through further modifying the park-breeze effect and thermal stress on vegetation. Key BGI features enhancing cooling efficiency include size, vegetation density, height, multi-level structure, and spatial configuration. Green roofs and walls are less effective than traditional BGI types. It remains unclear whether the shape complexity positively influences the cooling ability of BGI in every morphological context.

Methods such as remote sensing, numerical simulations, and field measurements vary in analytical capabilities, which differentiates their effectiveness to fulfill specific research needs. The remote sensing approach compares LST inside and around BGI, numerical simulations analyze mean temperature changes before and after BGI implementation, and field measurements focus on T-air differences within specific locations. Results vary mainly due to geographical and climatic differences, as well as in reference to cooling efficiency definitions, data resolution, BGI delimitation methods, and the types of simulation models used. Remote sensing analyses tend to overestimate cooling effects, especially with low-resolution data.

Key research gaps include deepening the understanding of the urban morphology's impact on the modification of BGI cooling effects and integrating new spatial data, including night-time and UAV LST, and meteorological data from IoT. A promising research direction is the development of Digital Twin technology for BGI planning and management optimization. However, challenges remain in implementing this technology, including data and model semantics, data quality, and real-time simulations.